Overview

Dataset statistics

Number of variables22
Number of observations857
Missing cells1087
Missing cells (%)5.8%
Duplicate rows80
Duplicate rows (%)9.3%
Total size in memory147.4 KiB
Average record size in memory176.1 B

Variable types

Categorical8
Numeric14

Alerts

Dataset has 80 (9.3%) duplicate rowsDuplicates
Channel Name has a high cardinality: 200 distinct values High cardinality
username has a high cardinality: 200 distinct values High cardinality
More topics has a high cardinality: 90 distinct values High cardinality
Youtube Link has a high cardinality: 200 distinct values High cardinality
followers is highly correlated with ViewsHigh correlation
Likes is highly correlated with Comments AvgHigh correlation
Engagement Rate is highly correlated with Engagement Rate 60days and 1 other fieldsHigh correlation
Engagement Rate 60days is highly correlated with Engagement Rate and 6 other fieldsHigh correlation
Views is highly correlated with followersHigh correlation
Views Avg. is highly correlated with Engagement Rate and 1 other fieldsHigh correlation
Avg. 1 Day is highly correlated with Avg. 3 Day and 4 other fieldsHigh correlation
Avg. 3 Day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Avg. 7 Day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Avg. 14 Day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Avg. 30 day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Avg. 60 day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Comments Avg is highly correlated with LikesHigh correlation
followers is highly correlated with Likes and 1 other fieldsHigh correlation
Likes is highly correlated with followersHigh correlation
Engagement Rate is highly correlated with Views Avg.High correlation
Engagement Rate 60days is highly correlated with Views Avg. and 6 other fieldsHigh correlation
Views is highly correlated with followersHigh correlation
Views Avg. is highly correlated with Engagement Rate and 5 other fieldsHigh correlation
Avg. 1 Day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Avg. 3 Day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Avg. 7 Day is highly correlated with Engagement Rate 60days and 6 other fieldsHigh correlation
Avg. 14 Day is highly correlated with Engagement Rate 60days and 6 other fieldsHigh correlation
Avg. 30 day is highly correlated with Engagement Rate 60days and 6 other fieldsHigh correlation
Avg. 60 day is highly correlated with Engagement Rate 60days and 6 other fieldsHigh correlation
Engagement Rate is highly correlated with Engagement Rate 60days and 1 other fieldsHigh correlation
Engagement Rate 60days is highly correlated with Engagement Rate and 5 other fieldsHigh correlation
Views Avg. is highly correlated with Engagement Rate and 1 other fieldsHigh correlation
Avg. 1 Day is highly correlated with Avg. 3 Day and 4 other fieldsHigh correlation
Avg. 3 Day is highly correlated with Avg. 1 Day and 4 other fieldsHigh correlation
Avg. 7 Day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Avg. 14 Day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Avg. 30 day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Avg. 60 day is highly correlated with Engagement Rate 60days and 5 other fieldsHigh correlation
Country is highly correlated with More topics and 1 other fieldsHigh correlation
More topics is highly correlated with Country and 3 other fieldsHigh correlation
Main topic is highly correlated with More topics and 1 other fieldsHigh correlation
Main Video Category is highly correlated with More topics and 1 other fieldsHigh correlation
Category is highly correlated with Country and 1 other fieldsHigh correlation
Country is highly correlated with Category and 16 other fieldsHigh correlation
Category is highly correlated with Country and 5 other fieldsHigh correlation
Main Video Category is highly correlated with Country and 14 other fieldsHigh correlation
followers is highly correlated with Country and 11 other fieldsHigh correlation
Main topic is highly correlated with Country and 17 other fieldsHigh correlation
More topics is highly correlated with Country and 17 other fieldsHigh correlation
Likes is highly correlated with Country and 11 other fieldsHigh correlation
Boost Index is highly correlated with Country and 4 other fieldsHigh correlation
Engagement Rate is highly correlated with Country and 7 other fieldsHigh correlation
Engagement Rate 60days is highly correlated with Country and 14 other fieldsHigh correlation
Views is highly correlated with Category and 10 other fieldsHigh correlation
Views Avg. is highly correlated with Country and 12 other fieldsHigh correlation
Avg. 1 Day is highly correlated with Country and 13 other fieldsHigh correlation
Avg. 3 Day is highly correlated with Country and 14 other fieldsHigh correlation
Avg. 7 Day is highly correlated with Country and 13 other fieldsHigh correlation
Avg. 14 Day is highly correlated with Country and 12 other fieldsHigh correlation
Avg. 30 day is highly correlated with Country and 13 other fieldsHigh correlation
Avg. 60 day is highly correlated with Country and 14 other fieldsHigh correlation
Comments Avg is highly correlated with Country and 12 other fieldsHigh correlation
Country has 150 (17.5%) missing values Missing
Category has 121 (14.1%) missing values Missing
Avg. 1 Day has 363 (42.4%) missing values Missing
Avg. 3 Day has 205 (23.9%) missing values Missing
Avg. 7 Day has 116 (13.5%) missing values Missing
Avg. 14 Day has 78 (9.1%) missing values Missing
Avg. 30 day has 42 (4.9%) missing values Missing
Avg. 1 Day has 104 (12.1%) zeros Zeros
Avg. 3 Day has 104 (12.1%) zeros Zeros
Avg. 7 Day has 104 (12.1%) zeros Zeros
Avg. 14 Day has 104 (12.1%) zeros Zeros
Avg. 30 day has 104 (12.1%) zeros Zeros
Avg. 60 day has 104 (12.1%) zeros Zeros
Comments Avg has 59 (6.9%) zeros Zeros

Reproduction

Analysis started2022-08-03 09:19:46.595753
Analysis finished2022-08-03 09:21:23.755146
Duration1 minute and 37.16 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Country
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)3.8%
Missing150
Missing (%)17.5%
Memory size6.8 KiB
US
277 
IN
212 
BR
46 
KR
 
18
MX
 
18
Other values (22)
136 

Length

Max length3
Median length2
Mean length2.002828854
Min length2

Characters and Unicode

Total characters1416
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIN
2nd rowUS
3rd rowIN
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US277
32.3%
IN212
24.7%
BR46
 
5.4%
KR18
 
2.1%
MX18
 
2.1%
RU16
 
1.9%
CA14
 
1.6%
TH12
 
1.4%
PH10
 
1.2%
PR10
 
1.2%
Other values (17)74
 
8.6%
(Missing)150
17.5%

Length

2022-08-03T14:51:24.272714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
us277
39.2%
in212
30.0%
br46
 
6.5%
kr18
 
2.5%
mx18
 
2.5%
ru16
 
2.3%
ca14
 
2.0%
th12
 
1.7%
ph10
 
1.4%
pr10
 
1.4%
Other values (16)74
 
10.5%

Most occurring characters

ValueCountFrequency (%)
U295
20.8%
S293
20.7%
I224
15.8%
N222
15.7%
R92
 
6.5%
B56
 
4.0%
C28
 
2.0%
K22
 
1.6%
A22
 
1.6%
H22
 
1.6%
Other values (13)140
9.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1414
99.9%
Control2
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U295
20.9%
S293
20.7%
I224
15.8%
N222
15.7%
R92
 
6.5%
B56
 
4.0%
C28
 
2.0%
K22
 
1.6%
A22
 
1.6%
H22
 
1.6%
Other values (12)138
9.8%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1414
99.9%
Common2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
U295
20.9%
S293
20.7%
I224
15.8%
N222
15.7%
R92
 
6.5%
B56
 
4.0%
C28
 
2.0%
K22
 
1.6%
A22
 
1.6%
H22
 
1.6%
Other values (12)138
9.8%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U295
20.8%
S293
20.7%
I224
15.8%
N222
15.7%
R92
 
6.5%
B56
 
4.0%
C28
 
2.0%
K22
 
1.6%
A22
 
1.6%
H22
 
1.6%
Other values (13)140
9.9%

Channel Name
Categorical

HIGH CARDINALITY

Distinct200
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
T-Series
 
9
SET India
 
9
PewDiePie
 
9
MrBeast
 
9
Like Nastya
 
9
Other values (195)
812 

Length

Max length49
Median length40
Mean length13.59626604
Min length2

Characters and Unicode

Total characters11652
Distinct characters114
Distinct categories13 ?
Distinct scripts6 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT-Series
2nd rowABCkidTV - Nursery Rhymes
3rd rowSET India
4th rowPewDiePie
5th rowMrBeast

Common Values

ValueCountFrequency (%)
T-Series9
 
1.1%
SET India9
 
1.1%
PewDiePie9
 
1.1%
MrBeast9
 
1.1%
Like Nastya9
 
1.1%
✿ Kids Diana Show9
 
1.1%
ABCkidTV - Nursery Rhymes9
 
1.1%
Alan Walker8
 
0.9%
elrubiusOMG8
 
0.9%
Speed Records8
 
0.9%
Other values (190)770
89.8%

Length

2022-08-03T14:51:24.726325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
91
 
4.9%
kids61
 
3.3%
songs44
 
2.4%
rhymes39
 
2.1%
music38
 
2.1%
nursery37
 
2.0%
and32
 
1.7%
tv26
 
1.4%
show21
 
1.1%
t-series19
 
1.0%
Other values (312)1439
77.9%

Most occurring characters

ValueCountFrequency (%)
990
 
8.5%
e825
 
7.1%
i822
 
7.1%
a810
 
7.0%
n664
 
5.7%
o597
 
5.1%
s584
 
5.0%
r466
 
4.0%
l388
 
3.3%
t340
 
2.9%
Other values (104)5166
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7848
67.4%
Uppercase Letter2499
 
21.4%
Space Separator990
 
8.5%
Dash Punctuation94
 
0.8%
Other Punctuation78
 
0.7%
Other Letter64
 
0.5%
Decimal Number50
 
0.4%
Other Symbol9
 
0.1%
Spacing Mark8
 
0.1%
Open Punctuation4
 
< 0.1%
Other values (3)8
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e825
10.5%
i822
10.5%
a810
 
10.3%
n664
 
8.5%
o597
 
7.6%
s584
 
7.4%
r466
 
5.9%
l388
 
4.9%
t340
 
4.3%
d305
 
3.9%
Other values (28)2047
26.1%
Other Letter
ValueCountFrequency (%)
ا6
 
9.4%
ة4
 
6.2%
ن4
 
6.2%
4
 
6.2%
2
 
3.1%
ف2
 
3.1%
2
 
3.1%
2
 
3.1%
2
 
3.1%
2
 
3.1%
Other values (17)34
53.1%
Uppercase Letter
ValueCountFrequency (%)
S281
 
11.2%
T185
 
7.4%
B168
 
6.7%
M155
 
6.2%
N148
 
5.9%
E135
 
5.4%
C121
 
4.8%
V121
 
4.8%
K117
 
4.7%
A117
 
4.7%
Other values (16)951
38.1%
Decimal Number
ValueCountFrequency (%)
510
20.0%
38
16.0%
68
16.0%
48
16.0%
16
12.0%
76
12.0%
24
 
8.0%
Other Punctuation
ValueCountFrequency (%)
&30
38.5%
'22
28.2%
.16
20.5%
?8
 
10.3%
:2
 
2.6%
Spacing Mark
ValueCountFrequency (%)
4
50.0%
ि4
50.0%
Open Punctuation
ValueCountFrequency (%)
[2
50.0%
(2
50.0%
Close Punctuation
ValueCountFrequency (%)
)2
50.0%
]2
50.0%
Space Separator
ValueCountFrequency (%)
990
100.0%
Dash Punctuation
ValueCountFrequency (%)
-94
100.0%
Other Symbol
ValueCountFrequency (%)
9
100.0%
Nonspacing Mark
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
|2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10251
88.0%
Common1231
 
10.6%
Cyrillic96
 
0.8%
Arabic32
 
0.3%
Devanagari26
 
0.2%
Hangul16
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e825
 
8.0%
i822
 
8.0%
a810
 
7.9%
n664
 
6.5%
o597
 
5.8%
s584
 
5.7%
r466
 
4.5%
l388
 
3.8%
t340
 
3.3%
d305
 
3.0%
Other values (46)4450
43.4%
Common
ValueCountFrequency (%)
990
80.4%
-94
 
7.6%
&30
 
2.4%
'22
 
1.8%
.16
 
1.3%
510
 
0.8%
9
 
0.7%
38
 
0.6%
68
 
0.6%
?8
 
0.6%
Other values (10)36
 
2.9%
Arabic
ValueCountFrequency (%)
ا6
18.8%
ة4
12.5%
ن4
12.5%
ف2
 
6.2%
ش2
 
6.2%
ق2
 
6.2%
ط2
 
6.2%
ي2
 
6.2%
و2
 
6.2%
ر2
 
6.2%
Other values (2)4
12.5%
Devanagari
ValueCountFrequency (%)
4
15.4%
ि4
15.4%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
Cyrillic
ValueCountFrequency (%)
М16
16.7%
а16
16.7%
е16
16.7%
д16
16.7%
ь8
8.3%
в8
8.3%
и8
8.3%
ш8
8.3%
Hangul
ValueCountFrequency (%)
4
25.0%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII11455
98.3%
Cyrillic96
 
0.8%
Arabic32
 
0.3%
Devanagari26
 
0.2%
None18
 
0.2%
Hangul16
 
0.1%
Dingbats9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
990
 
8.6%
e825
 
7.2%
i822
 
7.2%
a810
 
7.1%
n664
 
5.8%
o597
 
5.2%
s584
 
5.1%
r466
 
4.1%
l388
 
3.4%
t340
 
3.0%
Other values (59)4969
43.4%
Cyrillic
ValueCountFrequency (%)
М16
16.7%
а16
16.7%
е16
16.7%
д16
16.7%
ь8
8.3%
в8
8.3%
и8
8.3%
ш8
8.3%
Dingbats
ValueCountFrequency (%)
9
100.0%
None
ValueCountFrequency (%)
ê8
44.4%
à2
 
11.1%
ç2
 
11.1%
é2
 
11.1%
í2
 
11.1%
ó2
 
11.1%
Arabic
ValueCountFrequency (%)
ا6
18.8%
ة4
12.5%
ن4
12.5%
ف2
 
6.2%
ش2
 
6.2%
ق2
 
6.2%
ط2
 
6.2%
ي2
 
6.2%
و2
 
6.2%
ر2
 
6.2%
Other values (2)4
12.5%
Devanagari
ValueCountFrequency (%)
4
15.4%
ि4
15.4%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
Hangul
ValueCountFrequency (%)
4
25.0%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%

Category
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)1.1%
Missing121
Missing (%)14.1%
Memory size6.8 KiB
Gaming & Apps
398 
Music
210 
None
62 
Beauty & Fashion
 
36
Sports
 
8
Other values (3)
 
22

Length

Max length16
Median length13
Mean length9.942934783
Min length4

Characters and Unicode

Total characters7318
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGaming & Apps
2nd rowGaming & Apps
3rd rowGaming & Apps
4th rowGaming & Apps
5th rowGaming & Apps

Common Values

ValueCountFrequency (%)
Gaming & Apps398
46.4%
Music210
24.5%
None62
 
7.2%
Beauty & Fashion36
 
4.2%
Sports8
 
0.9%
Science & Tech8
 
0.9%
Fashion8
 
0.9%
LifeStyle6
 
0.7%
(Missing)121
 
14.1%

Length

2022-08-03T14:51:25.191147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-03T14:51:25.686251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
442
27.3%
gaming398
24.6%
apps398
24.6%
music210
13.0%
none62
 
3.8%
fashion44
 
2.7%
beauty36
 
2.2%
sports8
 
0.5%
science8
 
0.5%
tech8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
884
12.1%
p804
11.0%
i666
 
9.1%
s660
 
9.0%
n512
 
7.0%
a478
 
6.5%
&442
 
6.0%
G398
 
5.4%
m398
 
5.4%
g398
 
5.4%
Other values (18)1678
22.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4808
65.7%
Uppercase Letter1184
 
16.2%
Space Separator884
 
12.1%
Other Punctuation442
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p804
16.7%
i666
13.9%
s660
13.7%
n512
10.6%
a478
9.9%
m398
8.3%
g398
8.3%
u246
 
5.1%
c234
 
4.9%
e134
 
2.8%
Other values (7)278
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
G398
33.6%
A398
33.6%
M210
17.7%
N62
 
5.2%
F44
 
3.7%
B36
 
3.0%
S22
 
1.9%
T8
 
0.7%
L6
 
0.5%
Space Separator
ValueCountFrequency (%)
884
100.0%
Other Punctuation
ValueCountFrequency (%)
&442
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5992
81.9%
Common1326
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
p804
13.4%
i666
11.1%
s660
11.0%
n512
8.5%
a478
8.0%
G398
 
6.6%
m398
 
6.6%
g398
 
6.6%
A398
 
6.6%
u246
 
4.1%
Other values (16)1034
17.3%
Common
ValueCountFrequency (%)
884
66.7%
&442
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
884
12.1%
p804
11.0%
i666
 
9.1%
s660
 
9.0%
n512
 
7.0%
a478
 
6.5%
&442
 
6.0%
G398
 
5.4%
m398
 
5.4%
g398
 
5.4%
Other values (18)1678
22.9%

Main Video Category
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)1.9%
Missing2
Missing (%)0.2%
Memory size6.8 KiB
Music
315 
Entertainment
202 
Gaming
57 
People & Blogs
55 
Education
53 
Other values (11)
173 

Length

Max length21
Median length19
Mean length8.878362573
Min length5

Characters and Unicode

Total characters7591
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMusic
2nd rowEducation
3rd rowShows
4th rowGaming
5th rowEntertainment

Common Values

ValueCountFrequency (%)
Music315
36.8%
Entertainment202
23.6%
Gaming57
 
6.7%
People & Blogs55
 
6.4%
Education53
 
6.2%
Comedy46
 
5.4%
Film & Animation40
 
4.7%
Howto & Style22
 
2.6%
Shows19
 
2.2%
Sports16
 
1.9%
Other values (6)30
 
3.5%

Length

2022-08-03T14:51:26.130368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
music325
28.6%
entertainment202
17.8%
131
11.5%
gaming57
 
5.0%
people55
 
4.8%
blogs55
 
4.8%
education53
 
4.7%
comedy46
 
4.1%
film40
 
3.5%
animation40
 
3.5%
Other values (14)131
11.5%

Most occurring characters

ValueCountFrequency (%)
n802
 
10.6%
i791
 
10.4%
t783
 
10.3%
e606
 
8.0%
s447
 
5.9%
m401
 
5.3%
c394
 
5.2%
u380
 
5.0%
o360
 
4.7%
a360
 
4.7%
Other values (26)2267
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6190
81.5%
Uppercase Letter988
 
13.0%
Space Separator280
 
3.7%
Other Punctuation131
 
1.7%
Dash Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n802
13.0%
i791
12.8%
t783
12.6%
e606
9.8%
s447
7.2%
m401
6.5%
c394
6.4%
u380
6.1%
o360
5.8%
a360
5.8%
Other values (10)866
14.0%
Uppercase Letter
ValueCountFrequency (%)
M315
31.9%
E255
25.8%
P77
 
7.8%
S59
 
6.0%
G57
 
5.8%
B55
 
5.6%
C46
 
4.7%
A44
 
4.5%
F40
 
4.0%
H22
 
2.2%
Other values (3)18
 
1.8%
Space Separator
ValueCountFrequency (%)
280
100.0%
Other Punctuation
ValueCountFrequency (%)
&131
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7178
94.6%
Common413
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n802
11.2%
i791
11.0%
t783
10.9%
e606
 
8.4%
s447
 
6.2%
m401
 
5.6%
c394
 
5.5%
u380
 
5.3%
o360
 
5.0%
a360
 
5.0%
Other values (23)1854
25.8%
Common
ValueCountFrequency (%)
280
67.8%
&131
31.7%
-2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n802
 
10.6%
i791
 
10.4%
t783
 
10.3%
e606
 
8.0%
s447
 
5.9%
m401
 
5.3%
c394
 
5.2%
u380
 
5.0%
o360
 
4.7%
a360
 
4.7%
Other values (26)2267
29.9%

username
Categorical

HIGH CARDINALITY

Distinct200
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
T-Series
 
9
SET India
 
9
PewDiePie
 
9
MrBeast
 
9
Like Nastya
 
9
Other values (195)
812 

Length

Max length49
Median length40
Mean length13.59626604
Min length2

Characters and Unicode

Total characters11652
Distinct characters114
Distinct categories13 ?
Distinct scripts6 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT-Series
2nd rowABCkidTV - Nursery Rhymes
3rd rowSET India
4th rowPewDiePie
5th rowMrBeast

Common Values

ValueCountFrequency (%)
T-Series9
 
1.1%
SET India9
 
1.1%
PewDiePie9
 
1.1%
MrBeast9
 
1.1%
Like Nastya9
 
1.1%
✿ Kids Diana Show9
 
1.1%
ABCkidTV - Nursery Rhymes9
 
1.1%
Alan Walker8
 
0.9%
elrubiusOMG8
 
0.9%
Speed Records8
 
0.9%
Other values (190)770
89.8%

Length

2022-08-03T14:51:26.602492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
91
 
4.9%
kids61
 
3.3%
songs44
 
2.4%
rhymes39
 
2.1%
music38
 
2.1%
nursery37
 
2.0%
and32
 
1.7%
tv26
 
1.4%
show21
 
1.1%
t-series19
 
1.0%
Other values (312)1439
77.9%

Most occurring characters

ValueCountFrequency (%)
990
 
8.5%
e825
 
7.1%
i822
 
7.1%
a810
 
7.0%
n664
 
5.7%
o597
 
5.1%
s584
 
5.0%
r466
 
4.0%
l388
 
3.3%
t340
 
2.9%
Other values (104)5166
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7848
67.4%
Uppercase Letter2499
 
21.4%
Space Separator990
 
8.5%
Dash Punctuation94
 
0.8%
Other Punctuation78
 
0.7%
Other Letter64
 
0.5%
Decimal Number50
 
0.4%
Other Symbol9
 
0.1%
Spacing Mark8
 
0.1%
Open Punctuation4
 
< 0.1%
Other values (3)8
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e825
10.5%
i822
10.5%
a810
 
10.3%
n664
 
8.5%
o597
 
7.6%
s584
 
7.4%
r466
 
5.9%
l388
 
4.9%
t340
 
4.3%
d305
 
3.9%
Other values (28)2047
26.1%
Other Letter
ValueCountFrequency (%)
ا6
 
9.4%
ة4
 
6.2%
ن4
 
6.2%
4
 
6.2%
2
 
3.1%
ف2
 
3.1%
2
 
3.1%
2
 
3.1%
2
 
3.1%
2
 
3.1%
Other values (17)34
53.1%
Uppercase Letter
ValueCountFrequency (%)
S281
 
11.2%
T185
 
7.4%
B168
 
6.7%
M155
 
6.2%
N148
 
5.9%
E135
 
5.4%
C121
 
4.8%
V121
 
4.8%
K117
 
4.7%
A117
 
4.7%
Other values (16)951
38.1%
Decimal Number
ValueCountFrequency (%)
510
20.0%
38
16.0%
68
16.0%
48
16.0%
16
12.0%
76
12.0%
24
 
8.0%
Other Punctuation
ValueCountFrequency (%)
&30
38.5%
'22
28.2%
.16
20.5%
?8
 
10.3%
:2
 
2.6%
Spacing Mark
ValueCountFrequency (%)
4
50.0%
ि4
50.0%
Open Punctuation
ValueCountFrequency (%)
[2
50.0%
(2
50.0%
Close Punctuation
ValueCountFrequency (%)
)2
50.0%
]2
50.0%
Space Separator
ValueCountFrequency (%)
990
100.0%
Dash Punctuation
ValueCountFrequency (%)
-94
100.0%
Other Symbol
ValueCountFrequency (%)
9
100.0%
Nonspacing Mark
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
|2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10251
88.0%
Common1231
 
10.6%
Cyrillic96
 
0.8%
Arabic32
 
0.3%
Devanagari26
 
0.2%
Hangul16
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e825
 
8.0%
i822
 
8.0%
a810
 
7.9%
n664
 
6.5%
o597
 
5.8%
s584
 
5.7%
r466
 
4.5%
l388
 
3.8%
t340
 
3.3%
d305
 
3.0%
Other values (46)4450
43.4%
Common
ValueCountFrequency (%)
990
80.4%
-94
 
7.6%
&30
 
2.4%
'22
 
1.8%
.16
 
1.3%
510
 
0.8%
9
 
0.7%
38
 
0.6%
68
 
0.6%
?8
 
0.6%
Other values (10)36
 
2.9%
Arabic
ValueCountFrequency (%)
ا6
18.8%
ة4
12.5%
ن4
12.5%
ف2
 
6.2%
ش2
 
6.2%
ق2
 
6.2%
ط2
 
6.2%
ي2
 
6.2%
و2
 
6.2%
ر2
 
6.2%
Other values (2)4
12.5%
Devanagari
ValueCountFrequency (%)
4
15.4%
ि4
15.4%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
Cyrillic
ValueCountFrequency (%)
М16
16.7%
а16
16.7%
е16
16.7%
д16
16.7%
ь8
8.3%
в8
8.3%
и8
8.3%
ш8
8.3%
Hangul
ValueCountFrequency (%)
4
25.0%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII11455
98.3%
Cyrillic96
 
0.8%
Arabic32
 
0.3%
Devanagari26
 
0.2%
None18
 
0.2%
Hangul16
 
0.1%
Dingbats9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
990
 
8.6%
e825
 
7.2%
i822
 
7.2%
a810
 
7.1%
n664
 
5.8%
o597
 
5.2%
s584
 
5.1%
r466
 
4.1%
l388
 
3.4%
t340
 
3.0%
Other values (59)4969
43.4%
Cyrillic
ValueCountFrequency (%)
М16
16.7%
а16
16.7%
е16
16.7%
д16
16.7%
ь8
8.3%
в8
8.3%
и8
8.3%
ш8
8.3%
Dingbats
ValueCountFrequency (%)
9
100.0%
None
ValueCountFrequency (%)
ê8
44.4%
à2
 
11.1%
ç2
 
11.1%
é2
 
11.1%
í2
 
11.1%
ó2
 
11.1%
Arabic
ValueCountFrequency (%)
ا6
18.8%
ة4
12.5%
ن4
12.5%
ف2
 
6.2%
ش2
 
6.2%
ق2
 
6.2%
ط2
 
6.2%
ي2
 
6.2%
و2
 
6.2%
ر2
 
6.2%
Other values (2)4
12.5%
Devanagari
ValueCountFrequency (%)
4
15.4%
ि4
15.4%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
2
7.7%
Hangul
ValueCountFrequency (%)
4
25.0%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%

followers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct145
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49529754.96
Minimum24000000
Maximum220000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:27.092298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum24000000
5-th percentile25200000
Q133100000
median41300000
Q353400000
95-th percentile98100000
Maximum220000000
Range196000000
Interquartile range (IQR)20300000

Descriptive statistics

Standard deviation28619020.03
Coefficient of variation (CV)0.5778146905
Kurtosis12.88992816
Mean49529754.96
Median Absolute Deviation (MAD)10700000
Skewness3.053409439
Sum4.2447 × 1010
Variance8.190483075 × 1014
MonotonicityNot monotonic
2022-08-03T14:51:27.764475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3830000016
 
1.9%
3740000016
 
1.9%
4050000016
 
1.9%
3750000016
 
1.9%
4590000016
 
1.9%
2780000010
 
1.2%
973000009
 
1.1%
981000009
 
1.1%
1370000009
 
1.1%
972000009
 
1.1%
Other values (135)731
85.3%
ValueCountFrequency (%)
240000008
0.9%
241000002
 
0.2%
242000004
0.5%
244000004
0.5%
245000004
0.5%
246000002
 
0.2%
247000004
0.5%
248000004
0.5%
249000006
0.7%
250000004
0.5%
ValueCountFrequency (%)
2200000009
1.1%
1380000009
1.1%
1370000009
1.1%
1110000009
1.1%
981000009
1.1%
973000009
1.1%
972000009
1.1%
894000008
0.9%
855000008
0.9%
835000008
0.9%

Main topic
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)2.7%
Missing2
Missing (%)0.2%
Memory size6.8 KiB
Music
168 
Entertainment
138 
Lifestyle
135 
Movies
96 
Pop music
64 
Other values (18)
254 

Length

Max length23
Median length21
Mean length9.257309942
Min length4

Characters and Unicode

Total characters7915
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMusic of Asia
2nd rowMovies
3rd rowMovies
4th rowLifestyle
5th rowLifestyle

Common Values

ValueCountFrequency (%)
Music168
19.6%
Entertainment138
16.1%
Lifestyle135
15.8%
Movies96
11.2%
Pop music64
 
7.5%
Music of Asia47
 
5.5%
Hip hop music34
 
4.0%
TV shows28
 
3.3%
Action game22
 
2.6%
Hobby21
 
2.5%
Other values (13)102
11.9%

Length

2022-08-03T14:51:28.234471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
music343
28.2%
entertainment138
11.3%
lifestyle135
 
11.1%
movies96
 
7.9%
pop64
 
5.3%
of61
 
5.0%
asia47
 
3.9%
hip34
 
2.8%
hop34
 
2.8%
game32
 
2.6%
Other values (21)233
19.1%

Most occurring characters

ValueCountFrequency (%)
i905
 
11.4%
e768
 
9.7%
s685
 
8.7%
t631
 
8.0%
n520
 
6.6%
c447
 
5.6%
o412
 
5.2%
362
 
4.6%
u353
 
4.5%
M325
 
4.1%
Other values (27)2507
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6587
83.2%
Uppercase Letter958
 
12.1%
Space Separator362
 
4.6%
Dash Punctuation8
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i905
13.7%
e768
11.7%
s685
10.4%
t631
9.6%
n520
7.9%
c447
 
6.8%
o412
 
6.3%
u353
 
5.4%
m322
 
4.9%
a279
 
4.2%
Other values (12)1265
19.2%
Uppercase Letter
ValueCountFrequency (%)
M325
33.9%
E152
15.9%
L149
15.6%
A87
 
9.1%
P64
 
6.7%
H55
 
5.7%
T46
 
4.8%
V30
 
3.1%
G18
 
1.9%
S16
 
1.7%
Other values (3)16
 
1.7%
Space Separator
ValueCountFrequency (%)
362
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7545
95.3%
Common370
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i905
12.0%
e768
 
10.2%
s685
 
9.1%
t631
 
8.4%
n520
 
6.9%
c447
 
5.9%
o412
 
5.5%
u353
 
4.7%
M325
 
4.3%
m322
 
4.3%
Other values (25)2177
28.9%
Common
ValueCountFrequency (%)
362
97.8%
-8
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII7915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i905
 
11.4%
e768
 
9.7%
s685
 
8.7%
t631
 
8.0%
n520
 
6.6%
c447
 
5.6%
o412
 
5.2%
362
 
4.6%
u353
 
4.5%
M325
 
4.1%
Other values (27)2507
31.7%

More topics
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)10.5%
Missing2
Missing (%)0.2%
Memory size6.8 KiB
Entertainment,TV shows,Movies
 
58
Music,Movies,Entertainment
 
50
Music,Pop music
 
48
Lifestyle,Hobby
 
40
Entertainment,Music,Movies
 
29
Other values (85)
630 

Length

Max length109
Median length70
Mean length31.27602339
Min length5

Characters and Unicode

Total characters26741
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntertainment,Music of Asia,Music,Movies
2nd rowEntertainment,Music,Movies
3rd rowEntertainment,TV shows,Music,Movies
4th rowGaming,Action game,Lifestyle,Action-adventure game
5th rowEntertainment,Lifestyle,Technology

Common Values

ValueCountFrequency (%)
Entertainment,TV shows,Movies58
 
6.8%
Music,Movies,Entertainment50
 
5.8%
Music,Pop music48
 
5.6%
Lifestyle,Hobby40
 
4.7%
Entertainment,Music,Movies29
 
3.4%
Music,Music of Asia28
 
3.3%
Entertainment,Movies28
 
3.3%
Entertainment,Music of Asia,Music,Pop music24
 
2.8%
Lifestyle,Technology24
 
2.8%
Music,Movies,Music of Asia,Entertainment22
 
2.6%
Other values (80)504
58.8%

Length

2022-08-03T14:51:28.714598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
music242
 
11.1%
of193
 
8.8%
music,pop128
 
5.9%
hop110
 
5.0%
music,music98
 
4.5%
music,hip86
 
3.9%
entertainment,tv77
 
3.5%
shows,movies58
 
2.7%
latin54
 
2.5%
music,movies,entertainment50
 
2.3%
Other values (93)1090
49.9%

Most occurring characters

ValueCountFrequency (%)
i2731
 
10.2%
e2202
 
8.2%
s2032
 
7.6%
t1841
 
6.9%
n1769
 
6.6%
,1707
 
6.4%
o1469
 
5.5%
c1415
 
5.3%
1331
 
5.0%
m1145
 
4.3%
Other values (31)9099
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20692
77.4%
Uppercase Letter2926
 
10.9%
Other Punctuation1707
 
6.4%
Space Separator1331
 
5.0%
Dash Punctuation85
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i2731
13.2%
e2202
10.6%
s2032
9.8%
t1841
8.9%
n1769
8.5%
o1469
 
7.1%
c1415
 
6.8%
m1145
 
5.5%
u1141
 
5.5%
a1019
 
4.9%
Other values (13)3928
19.0%
Uppercase Letter
ValueCountFrequency (%)
M969
33.1%
E472
16.1%
A307
 
10.5%
L264
 
9.0%
P240
 
8.2%
H192
 
6.6%
T164
 
5.6%
V121
 
4.1%
G67
 
2.3%
R66
 
2.3%
Other values (5)64
 
2.2%
Other Punctuation
ValueCountFrequency (%)
,1707
100.0%
Space Separator
ValueCountFrequency (%)
1331
100.0%
Dash Punctuation
ValueCountFrequency (%)
-85
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23618
88.3%
Common3123
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i2731
 
11.6%
e2202
 
9.3%
s2032
 
8.6%
t1841
 
7.8%
n1769
 
7.5%
o1469
 
6.2%
c1415
 
6.0%
m1145
 
4.8%
u1141
 
4.8%
a1019
 
4.3%
Other values (28)6854
29.0%
Common
ValueCountFrequency (%)
,1707
54.7%
1331
42.6%
-85
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII26741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i2731
 
10.2%
e2202
 
8.2%
s2032
 
7.6%
t1841
 
6.9%
n1769
 
6.6%
,1707
 
6.4%
o1469
 
5.5%
c1415
 
5.3%
1331
 
5.0%
m1145
 
4.3%
Other values (31)9099
34.0%

Likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct199
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233099517.8
Minimum0
Maximum2191405542
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:29.187980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5322828.259
Q129263861.96
median95375135.95
Q3235190437.5
95-th percentile927031869.8
Maximum2191405542
Range2191405542
Interquartile range (IQR)205926575.6

Descriptive statistics

Standard deviation386246089.1
Coefficient of variation (CV)1.657000807
Kurtosis10.09433678
Mean233099517.8
Median Absolute Deviation (MAD)77280531.95
Skewness3.064739796
Sum1.997662868 × 1011
Variance1.491860413 × 1017
MonotonicityNot monotonic
2022-08-03T14:51:29.664674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16026801729
 
1.1%
174875242.69
 
1.1%
21914055429
 
1.1%
17318334619
 
1.1%
280877652.49
 
1.1%
235190437.59
 
1.1%
220990134.69
 
1.1%
55641776.548
 
0.9%
8148954438
 
0.9%
155670991.28
 
0.9%
Other values (189)770
89.8%
ValueCountFrequency (%)
04
0.5%
790615.85072
 
0.2%
11875502
 
0.2%
1205009.128
0.9%
22359042
 
0.2%
2304173.252
 
0.2%
2809022.952
 
0.2%
3356772.5582
 
0.2%
3395907.0712
 
0.2%
3656173.3332
 
0.2%
ValueCountFrequency (%)
21914055429
1.1%
17318334619
1.1%
16407375538
0.9%
16026801729
1.1%
937427149.98
0.9%
924433049.72
 
0.2%
8590705842
 
0.2%
855552544.82
 
0.2%
8148954438
0.9%
772750483.92
 
0.2%

Boost Index
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.39439907
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:30.148803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q162
median71
Q378
95-th percentile82
Maximum88
Range87
Interquartile range (IQR)16

Descriptive statistics

Standard deviation14.96322178
Coefficient of variation (CV)0.2220247081
Kurtosis3.173288016
Mean67.39439907
Median Absolute Deviation (MAD)8
Skewness-1.729874088
Sum57757
Variance223.898006
MonotonicityNot monotonic
2022-08-03T14:51:30.679053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7564
 
7.5%
8262
 
7.2%
7948
 
5.6%
7048
 
5.6%
7238
 
4.4%
6838
 
4.4%
7338
 
4.4%
6936
 
4.2%
6733
 
3.9%
6029
 
3.4%
Other values (46)423
49.4%
ValueCountFrequency (%)
12
 
0.2%
42
 
0.2%
168
0.9%
182
 
0.2%
202
 
0.2%
212
 
0.2%
242
 
0.2%
254
0.5%
278
0.9%
282
 
0.2%
ValueCountFrequency (%)
889
 
1.1%
852
 
0.2%
8323
 
2.7%
8262
7.2%
8124
 
2.8%
8026
3.0%
7948
5.6%
7828
3.3%
7714
 
1.6%
7616
 
1.9%

Engagement Rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct199
Distinct (%)23.3%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.3529577928
Minimum0.0002609916678
Maximum10.58408389
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:31.137460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0002609916678
5-th percentile0.002581305319
Q10.02604412878
median0.09893292202
Q30.3795535109
95-th percentile1.413863831
Maximum10.58408389
Range10.5838229
Interquartile range (IQR)0.3535093821

Descriptive statistics

Standard deviation0.7985090654
Coefficient of variation (CV)2.262335842
Kurtosis74.05611912
Mean0.3529577928
Median Absolute Deviation (MAD)0.08736157631
Skewness7.195574012
Sum301.7789129
Variance0.6376167275
MonotonicityNot monotonic
2022-08-03T14:51:31.597421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.033463131339
 
1.1%
0.0012062699239
 
1.1%
0.063426127459
 
1.1%
0.72920954969
 
1.1%
1.0267606749
 
1.1%
0.69034177519
 
1.1%
0.64171569839
 
1.1%
0.17152274568
 
0.9%
0.30742549028
 
0.9%
0.027481222398
 
0.9%
Other values (189)768
89.6%
ValueCountFrequency (%)
0.00026099166782
 
0.2%
0.00088918355182
 
0.2%
0.0012062699239
1.1%
0.0012839499172
 
0.2%
0.0015876762138
0.9%
0.0021966936022
 
0.2%
0.0024687181812
 
0.2%
0.0024718681698
0.9%
0.0025363618918
0.9%
0.0026005667898
0.9%
ValueCountFrequency (%)
10.584083892
 
0.2%
6.4317471962
 
0.2%
5.4284974212
 
0.2%
3.1235834042
 
0.2%
2.765772052
 
0.2%
2.7392385552
 
0.2%
2.6148652418
0.9%
2.2648135822
 
0.2%
2.1967147712
 
0.2%
2.0236181742
 
0.2%

Engagement Rate 60days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct200
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07412766932
Minimum0
Maximum1.519044268
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:32.051778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0009435665221
Q10.005434907422
median0.02067140183
Q30.06292054462
95-th percentile0.2873472397
Maximum1.519044268
Range1.519044268
Interquartile range (IQR)0.0574856372

Descriptive statistics

Standard deviation0.1764902187
Coefficient of variation (CV)2.380895289
Kurtosis34.07269598
Mean0.07412766932
Median Absolute Deviation (MAD)0.01806319627
Skewness5.371329405
Sum63.52741261
Variance0.0311487973
MonotonicityNot monotonic
2022-08-03T14:51:32.541276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.010879483479
 
1.1%
0.0023656925359
 
1.1%
0.0448460759
 
1.1%
0.57027022459
 
1.1%
0.23138805969
 
1.1%
0.24385312059
 
1.1%
0.11600368399
 
1.1%
0.022760062548
 
0.9%
0.029883824868
 
0.9%
0.0050243866718
 
0.9%
Other values (190)770
89.8%
ValueCountFrequency (%)
02
 
0.2%
2.836190936 × 10-52
 
0.2%
7.451865193 × 10-52
 
0.2%
0.00040823299498
0.9%
0.00042835524032
 
0.2%
0.00061244697882
 
0.2%
0.00063917211532
 
0.2%
0.00064128001812
 
0.2%
0.0006981779498
0.9%
0.0007625895162
 
0.2%
ValueCountFrequency (%)
1.5190442682
 
0.2%
1.3249810138
0.9%
0.98923458082
 
0.2%
0.57027022459
1.1%
0.48959575232
 
0.2%
0.43940955742
 
0.2%
0.43851092028
0.9%
0.36181724222
 
0.2%
0.3483647892
 
0.2%
0.30065430112
 
0.2%

Views
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct200
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.705917021 × 1010
Minimum0
Maximum1.956607444 × 1011
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:33.020943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4188413314
Q11.338296583 × 1010
median2.019496287 × 1010
Q32.834708423 × 1010
95-th percentile8.01115558 × 1010
Maximum1.956607444 × 1011
Range1.956607444 × 1011
Interquartile range (IQR)1.49641184 × 1010

Descriptive statistics

Standard deviation2.79723041 × 1010
Coefficient of variation (CV)1.03374582
Kurtosis15.09068444
Mean2.705917021 × 1010
Median Absolute Deviation (MAD)7447400969
Skewness3.480599436
Sum2.318970887 × 1013
Variance7.824497969 × 1020
MonotonicityNot monotonic
2022-08-03T14:51:33.514570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.956607444 × 10119
 
1.1%
1.217417393 × 10119
 
1.1%
2.842411394 × 10109
 
1.1%
1.624263427 × 10109
 
1.1%
8.01115558 × 10109
 
1.1%
7.734015558 × 10109
 
1.1%
1.330253255 × 10119
 
1.1%
1.161707244 × 10108
 
0.9%
74696799208
 
0.9%
2.415800586 × 10108
 
0.9%
Other values (190)770
89.8%
ValueCountFrequency (%)
02
0.2%
9944180442
0.2%
21860155332
0.2%
22270210342
0.2%
25827560552
0.2%
26179706932
0.2%
26964432322
0.2%
28607446082
0.2%
29253753072
0.2%
30465609722
0.2%
ValueCountFrequency (%)
1.956607444 × 10119
1.1%
1.330253255 × 10119
1.1%
1.217417393 × 10119
1.1%
8.043107329 × 10108
0.9%
8.01115558 × 10109
1.1%
7.734015558 × 10109
1.1%
6.979436375 × 10108
0.9%
6.514308031 × 10108
0.9%
5.843463317 × 10108
0.9%
5.141396266 × 10102
 
0.2%

Views Avg.
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct199
Distinct (%)23.3%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean16237903.56
Minimum680.6064558
Maximum423923499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:34.192424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum680.6064558
5-th percentile81556.07136
Q1711603.375
median2871914.5
Q315259819.58
95-th percentile88722202
Maximum423923499
Range423922818.4
Interquartile range (IQR)14548216.21

Descriptive statistics

Standard deviation35662558.75
Coefficient of variation (CV)2.196253884
Kurtosis42.95501178
Mean16237903.56
Median Absolute Deviation (MAD)2733815.888
Skewness5.146593246
Sum1.388340754 × 1010
Variance1.271818097 × 1015
MonotonicityNot monotonic
2022-08-03T14:51:34.660544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2095329.1329
 
1.1%
109572.87849
 
1.1%
7718344.8479
 
1.1%
98762497.259
 
1.1%
138163704.59
 
1.1%
86417353.259
 
1.1%
70271260.259
 
1.1%
4189688.758
 
0.9%
165939058
 
0.9%
155542.61138
 
0.9%
Other values (189)768
89.6%
ValueCountFrequency (%)
680.60645582
 
0.2%
15583.36322
 
0.2%
19848.899892
 
0.2%
24789.70348
0.9%
467302
 
0.2%
48706.466672
 
0.2%
57639.06768
0.9%
59493.506172
 
0.2%
62705.111112
 
0.2%
65172.486122
 
0.2%
ValueCountFrequency (%)
4239234992
 
0.2%
2054500482
 
0.2%
138163704.59
1.1%
1359413802
 
0.2%
1276057998
0.9%
108949716.58
0.9%
98762497.259
1.1%
959693132
 
0.2%
887222022
 
0.2%
86417353.259
1.1%

Avg. 1 Day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct89
Distinct (%)18.0%
Missing363
Missing (%)42.4%
Infinite0
Infinite (%)0.0%
Mean183817.5011
Minimum0
Maximum3472638
Zeros104
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:35.127600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13746.666667
median30542
Q399819.23438
95-th percentile1411121
Maximum3472638
Range3472638
Interquartile range (IQR)96072.56771

Descriptive statistics

Standard deviation437793.4125
Coefficient of variation (CV)2.381674269
Kurtosis17.14908693
Mean183817.5011
Median Absolute Deviation (MAD)30542
Skewness3.790256018
Sum90805845.55
Variance1.916630721 × 1011
MonotonicityNot monotonic
2022-08-03T14:51:35.603707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0104
 
12.1%
806210
 
1.2%
152244.83339
 
1.1%
18379169
 
1.1%
22960.333338
 
0.9%
60428
 
0.9%
624668
 
0.9%
265558
 
0.9%
472591.758
 
0.9%
61180.931038
 
0.9%
Other values (79)314
36.6%
(Missing)363
42.4%
ValueCountFrequency (%)
0104
12.1%
455.61627912
 
0.2%
21762
 
0.2%
2206.28
 
0.9%
2548.7142862
 
0.2%
3746.6666678
 
0.9%
3937.6842118
 
0.9%
5318.6666672
 
0.2%
56442
 
0.2%
60428
 
0.9%
ValueCountFrequency (%)
34726382
 
0.2%
18379169
1.1%
1638377.58
0.9%
14111218
0.9%
12774442
 
0.2%
8880528
0.9%
6602502
 
0.2%
6529352
 
0.2%
522267.52
 
0.2%
4859062
 
0.2%

Avg. 3 Day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct126
Distinct (%)19.3%
Missing205
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean571693.2539
Minimum0
Maximum6596001
Zeros104
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:36.094166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115686.5
median84666
Q3586040
95-th percentile3204908.25
Maximum6596001
Range6596001
Interquartile range (IQR)570353.5

Descriptive statistics

Standard deviation1145431.477
Coefficient of variation (CV)2.003577039
Kurtosis10.75381616
Mean571693.2539
Median Absolute Deviation (MAD)84666
Skewness3.116069931
Sum372744001.6
Variance1.312013269 × 1012
MonotonicityNot monotonic
2022-08-03T14:51:36.569466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0104
 
12.1%
2134569.6259
 
1.1%
5860409
 
1.1%
39468689
 
1.1%
18379169
 
1.1%
77417.910458
 
0.9%
421908
 
0.9%
608169.42868
 
0.9%
8880528
 
0.9%
50485.571438
 
0.9%
Other values (116)472
55.1%
(Missing)205
23.9%
ValueCountFrequency (%)
0104
12.1%
703.04733732
 
0.2%
2206.28
 
0.9%
2548.7142862
 
0.2%
4601.8307698
 
0.9%
56442
 
0.2%
6564.52
 
0.2%
80622
 
0.2%
8822.52
 
0.2%
11400.545458
 
0.9%
ValueCountFrequency (%)
65960018
0.9%
55107472
 
0.2%
43547008
0.9%
39468689
1.1%
34726382
 
0.2%
3204908.258
0.9%
2239180.58
0.9%
2134569.6259
1.1%
1979735.52
 
0.2%
18379169
1.1%

Avg. 7 Day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct145
Distinct (%)19.6%
Missing116
Missing (%)13.5%
Infinite0
Infinite (%)0.0%
Mean1385198.908
Minimum0
Maximum29941021
Zeros104
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:37.052222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128229.90148
median182415.6053
Q3921913.5
95-th percentile6833904
Maximum29941021
Range29941021
Interquartile range (IQR)893683.5985

Descriptive statistics

Standard deviation3684596.117
Coefficient of variation (CV)2.659976192
Kurtosis41.60599454
Mean1385198.908
Median Absolute Deviation (MAD)180901.4221
Skewness5.903128167
Sum1026432391
Variance1.357624855 × 1013
MonotonicityNot monotonic
2022-08-03T14:51:37.528352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0104
 
12.1%
1809830.3959
 
1.1%
280127.63649
 
1.1%
34973959
 
1.1%
299410219
 
1.1%
58299299
 
1.1%
7539489.59
 
1.1%
48918329
 
1.1%
37575.58
 
0.9%
77903.658
 
0.9%
Other values (135)558
65.1%
(Missing)116
 
13.5%
ValueCountFrequency (%)
0104
12.1%
1514.1831682
 
0.2%
2548.7142862
 
0.2%
4961.6569778
 
0.9%
56442
 
0.2%
8792.6666678
 
0.9%
10612.571432
 
0.2%
12586.62
 
0.2%
13738.047628
 
0.9%
13801.815922
 
0.2%
ValueCountFrequency (%)
299410219
1.1%
7539489.59
1.1%
7241542.258
0.9%
70178788
0.9%
68339048
0.9%
68284532
 
0.2%
64852768
0.9%
58299299
1.1%
55107472
 
0.2%
48918329
1.1%

Avg. 14 Day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct155
Distinct (%)19.9%
Missing78
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean2399226.525
Minimum0
Maximum68777316
Zeros104
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:38.002959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q144029.7906
median300547.2
Q31337818.571
95-th percentile8734760.635
Maximum68777316
Range68777316
Interquartile range (IQR)1293788.781

Descriptive statistics

Standard deviation7865794.09
Coefficient of variation (CV)3.278470794
Kurtosis51.46959957
Mean2399226.525
Median Absolute Deviation (MAD)300547.2
Skewness6.766159219
Sum1868997463
Variance6.187071667 × 1013
MonotonicityNot monotonic
2022-08-03T14:51:38.484410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0104
 
12.1%
2306178.2059
 
1.1%
343788.13339
 
1.1%
3094440.49
 
1.1%
299410219
 
1.1%
9480138.259
 
1.1%
9148087.759
 
1.1%
7052576.59
 
1.1%
7653188
 
0.9%
229318.94598
 
0.9%
Other values (145)596
69.5%
(Missing)78
 
9.1%
ValueCountFrequency (%)
0104
12.1%
2548.7142862
 
0.2%
2973.2986732
 
0.2%
5286.7156868
 
0.9%
56442
 
0.2%
12586.62
 
0.2%
13546.8752
 
0.2%
13801.815922
 
0.2%
16760.52
 
0.2%
191302
 
0.2%
ValueCountFrequency (%)
687773168
0.9%
299410219
1.1%
238615012
 
0.2%
9813003.5712
 
0.2%
9480138.259
1.1%
9148087.759
1.1%
8688835.48
0.9%
8214380.52
 
0.2%
8166221.758
0.9%
72326418
0.9%

Avg. 30 day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct164
Distinct (%)20.1%
Missing42
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean2969197.062
Minimum0
Maximum68777316
Zeros104
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:38.959229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q177479.05625
median381732.5312
Q31923808.571
95-th percentile13756907.67
Maximum68777316
Range68777316
Interquartile range (IQR)1846329.515

Descriptive statistics

Standard deviation8481571.241
Coefficient of variation (CV)2.856520151
Kurtosis35.94428278
Mean2969197.062
Median Absolute Deviation (MAD)381732.5312
Skewness5.532144105
Sum2419895606
Variance7.193705072 × 1013
MonotonicityNot monotonic
2022-08-03T14:51:39.439488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0104
 
12.1%
1676329.8419
 
1.1%
353601.85719
 
1.1%
3620273.59
 
1.1%
299410219
 
1.1%
13756907.679
 
1.1%
14347388.629
 
1.1%
12654329.679
 
1.1%
687773168
 
0.9%
170820.40288
 
0.9%
Other values (154)632
73.7%
(Missing)42
 
4.9%
ValueCountFrequency (%)
0104
12.1%
2508.6082
 
0.2%
56442
 
0.2%
6600.8992638
 
0.9%
13755.207922
 
0.2%
15105.294122
 
0.2%
15931.8752
 
0.2%
16167.657142
 
0.2%
191302
 
0.2%
25126.583338
 
0.9%
ValueCountFrequency (%)
687773168
0.9%
33821451.678
0.9%
299410219
1.1%
238615012
 
0.2%
14347388.629
1.1%
139437872
 
0.2%
13756907.679
1.1%
12654329.679
1.1%
10186832.52
 
0.2%
9949841.9468
0.9%

Avg. 60 day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct173
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3461323.172
Minimum0
Maximum53835227.96
Zeros104
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:40.099656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q199613.56667
median603554.8636
Q32295416.145
95-th percentile22108751.05
Maximum53835227.96
Range53835227.96
Interquartile range (IQR)2195802.578

Descriptive statistics

Standard deviation8740184.214
Coefficient of variation (CV)2.525099154
Kurtosis20.27838648
Mean3461323.172
Median Absolute Deviation (MAD)576918.6372
Skewness4.324355078
Sum2966353959
Variance7.63908201 × 1013
MonotonicityNot monotonic
2022-08-03T14:51:40.582772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0104
 
12.1%
2295416.1459
 
1.1%
322033.61029
 
1.1%
4454119.6259
 
1.1%
534347339
 
1.1%
22108751.059
 
1.1%
23467097.449
 
1.1%
15722844.719
 
1.1%
111150.84388
 
0.9%
329734.11118
 
0.9%
Other values (163)674
78.6%
ValueCountFrequency (%)
0104
12.1%
2553.0572572
 
0.2%
56442
 
0.2%
13935.965692
 
0.2%
15105.294122
 
0.2%
191302
 
0.2%
19564.105338
 
0.9%
20690.327582
 
0.2%
20762.015622
 
0.2%
21974.6252
 
0.2%
ValueCountFrequency (%)
53835227.968
0.9%
534347339
1.1%
387397542
 
0.2%
24476021.48
0.9%
238615012
 
0.2%
23467097.449
1.1%
22108751.059
1.1%
15722844.719
1.1%
12828550.672
 
0.2%
12541612.52
 
0.2%

Comments Avg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct186
Distinct (%)21.8%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean15463.09977
Minimum0
Maximum199523.4677
Zeros59
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2022-08-03T14:51:41.058894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q166.91314838
median1145.008734
Q312890.23188
95-th percentile98952.06735
Maximum199523.4677
Range199523.4677
Interquartile range (IQR)12823.31874

Descriptive statistics

Standard deviation32464.33085
Coefficient of variation (CV)2.099471085
Kurtosis7.908328967
Mean15463.09977
Median Absolute Deviation (MAD)1145.008734
Skewness2.850062031
Sum13220950.3
Variance1053932778
MonotonicityNot monotonic
2022-08-03T14:51:41.539024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
059
 
6.9%
4493.9841469
 
1.1%
76.244316359
 
1.1%
35839.781359
 
1.1%
113432.37379
 
1.1%
0.29249011869
 
1.1%
146.70025199
 
1.1%
2540.3448288
 
0.9%
1219.0380238
 
0.9%
6.4570637128
 
0.9%
Other values (176)718
83.8%
ValueCountFrequency (%)
059
6.9%
0.01027397262
 
0.2%
0.058
 
0.9%
0.062717770032
 
0.2%
0.29249011869
 
1.1%
0.66216216222
 
0.2%
0.78163771712
 
0.2%
1.439603962
 
0.2%
2.779629632
 
0.2%
4.0854271362
 
0.2%
ValueCountFrequency (%)
199523.46772
 
0.2%
148091.57148
0.9%
135847.30138
0.9%
133934.968
0.9%
113432.37379
1.1%
100248.52
 
0.2%
98952.067358
0.9%
97244.645578
0.9%
96580.604658
0.9%
93869.746032
 
0.2%

Youtube Link
Categorical

HIGH CARDINALITY

Distinct200
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
UCq-Fj5jknLsUf-MWSy4_brA
 
9
UCpEhnqL0y41EpW2TvWAHD7Q
 
9
UC-lHJZR3Gqxm24_Vd_AJ5Yw
 
9
UCX6OQ3DkcsbYNE6H8uQQuVA
 
9
UCJplp5SjeGSdVdwsfb9Q7lQ
 
9
Other values (195)
812 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters20568
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUCq-Fj5jknLsUf-MWSy4_brA
2nd rowUCbCmjCuTUZos6Inko4u57UQ
3rd rowUCpEhnqL0y41EpW2TvWAHD7Q
4th rowUC-lHJZR3Gqxm24_Vd_AJ5Yw
5th rowUCX6OQ3DkcsbYNE6H8uQQuVA

Common Values

ValueCountFrequency (%)
UCq-Fj5jknLsUf-MWSy4_brA9
 
1.1%
UCpEhnqL0y41EpW2TvWAHD7Q9
 
1.1%
UC-lHJZR3Gqxm24_Vd_AJ5Yw9
 
1.1%
UCX6OQ3DkcsbYNE6H8uQQuVA9
 
1.1%
UCJplp5SjeGSdVdwsfb9Q7lQ9
 
1.1%
UCk8GzjMOrta8yxDcKfylJYw9
 
1.1%
UCbCmjCuTUZos6Inko4u57UQ9
 
1.1%
UCJrOtniJ0-NWz37R30urifQ8
 
0.9%
UCXazgXDIYyWH-yXLAkcrFxw8
 
0.9%
UCOsyDsO5tIt-VZ1iwjdQmew8
 
0.9%
Other values (190)770
89.8%

Length

2022-08-03T14:51:41.984613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ucq-fj5jknlsuf-mwsy4_bra9
 
1.1%
uc-lhjzr3gqxm24_vd_aj5yw9
 
1.1%
ucx6oq3dkcsbyne6h8uqquva9
 
1.1%
ucjplp5sjegsdvdwsfb9q7lq9
 
1.1%
uck8gzjmorta8yxdckfyljyw9
 
1.1%
ucbcmjcutuzos6inko4u57uq9
 
1.1%
ucpehnql0y41epw2tvwahd7q9
 
1.1%
uc3gnmtgu-ttbfppfss5knkg8
 
0.9%
ucj5v_mcy6gnubto8-d3xoag8
 
0.9%
ucigm_e4zwyshv3bcw1pnseq8
 
0.9%
Other values (190)770
89.8%

Most occurring characters

ValueCountFrequency (%)
C1157
 
5.6%
U1102
 
5.4%
Q539
 
2.6%
w501
 
2.4%
A498
 
2.4%
g448
 
2.2%
p380
 
1.8%
6369
 
1.8%
O362
 
1.8%
-341
 
1.7%
Other values (54)14871
72.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9494
46.2%
Lowercase Letter7482
36.4%
Decimal Number2942
 
14.3%
Dash Punctuation341
 
1.7%
Connector Punctuation309
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C1157
 
12.2%
U1102
 
11.6%
Q539
 
5.7%
A498
 
5.2%
O362
 
3.8%
X335
 
3.5%
L322
 
3.4%
N321
 
3.4%
Y313
 
3.3%
Z310
 
3.3%
Other values (16)4235
44.6%
Lowercase Letter
ValueCountFrequency (%)
w501
 
6.7%
g448
 
6.0%
p380
 
5.1%
t339
 
4.5%
o334
 
4.5%
y324
 
4.3%
n295
 
3.9%
m290
 
3.9%
d285
 
3.8%
j283
 
3.8%
Other values (16)4003
53.5%
Decimal Number
ValueCountFrequency (%)
6369
12.5%
3334
11.4%
0311
10.6%
7309
10.5%
4290
9.9%
2286
9.7%
8281
9.6%
5266
9.0%
1263
8.9%
9233
7.9%
Dash Punctuation
ValueCountFrequency (%)
-341
100.0%
Connector Punctuation
ValueCountFrequency (%)
_309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16976
82.5%
Common3592
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
C1157
 
6.8%
U1102
 
6.5%
Q539
 
3.2%
w501
 
3.0%
A498
 
2.9%
g448
 
2.6%
p380
 
2.2%
O362
 
2.1%
t339
 
2.0%
X335
 
2.0%
Other values (42)11315
66.7%
Common
ValueCountFrequency (%)
6369
10.3%
-341
9.5%
3334
9.3%
0311
8.7%
_309
8.6%
7309
8.6%
4290
8.1%
2286
8.0%
8281
7.8%
5266
7.4%
Other values (2)496
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII20568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C1157
 
5.6%
U1102
 
5.4%
Q539
 
2.6%
w501
 
2.4%
A498
 
2.4%
g448
 
2.2%
p380
 
1.8%
6369
 
1.8%
O362
 
1.8%
-341
 
1.7%
Other values (54)14871
72.3%

Interactions

2022-08-03T14:51:14.922020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:53.415455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:59.721532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:05.761456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:11.906917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:18.014955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:24.857092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:31.342151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:38.009451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:43.906987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:49.903455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:56.132324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:02.526708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:08.702857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:15.373461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:53.937860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:00.157629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:06.193568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:12.351043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:18.459070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:25.229765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:31.803943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:38.387883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:44.335097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:50.387228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:56.576442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:03.011408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:09.123106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:15.803769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:54.356598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:00.573744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:06.601593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:12.715142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:18.892954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:25.631830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:32.389402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:38.788108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:44.751226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:50.801010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:57.019778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:03.445851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:09.519210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:16.235888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:54.788730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:00.994784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:07.009721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:13.096692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:19.317065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:26.040350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:32.809487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:39.187127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:45.159333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:51.213119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:57.452735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:03.871527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:09.947323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:16.664001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:55.312866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:01.414914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:07.425830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:13.508800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:19.749182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:26.453679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:33.201718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:39.595270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:45.583445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:51.639791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:57.888808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:04.314869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:10.396953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:17.111810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:55.770650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:01.878543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:07.856375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:14.128947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:20.187113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:26.905341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:33.942396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:40.019918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:46.013909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-08-03T14:51:11.059127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:17.574238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:56.222773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:02.284388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:08.284485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:14.558420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:20.630363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:27.469670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:34.475981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:40.454669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:46.442021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:52.704012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:58.810848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:05.189174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:11.503247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:18.006351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:56.656887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:02.672486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:08.696580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:14.990519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:21.058284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:27.950350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:35.012626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:40.875103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:46.861089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:53.128127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:59.230935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:05.605283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:11.929258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:18.438468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:57.085016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:03.072613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:09.110240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:15.394624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:21.481154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:28.379807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:35.448759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:41.275158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:47.291001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:53.544236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:59.648386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:06.038773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:12.361373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:18.871053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:57.513129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:03.527077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:09.555490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:15.816315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:21.968961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:28.840667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:35.924719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:41.670026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:47.715110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:53.970570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:00.080363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:06.460583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:12.781486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:19.303171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:57.949232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:03.919536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:10.150824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:16.292458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:22.365409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:29.298604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:36.389186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:42.064773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:48.143222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:54.398427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:00.508475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:06.891931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:13.191361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:19.743266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:58.390778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:04.491665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:10.576754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:16.755990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:22.798983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:29.817471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:36.825338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:42.448874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:48.556966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:54.826539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:00.930809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:07.324047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:13.587464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:20.386057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:58.834913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:04.909229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:11.012894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:17.158711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:23.424258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:30.369625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:37.229443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:43.049033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:48.985076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:55.270655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:01.554974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:07.770112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:14.087186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:20.818171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:49:59.271027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:05.329319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:11.461277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:17.578838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:24.173448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:30.807811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:37.630754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:43.478871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:49.431594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:50:55.692208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:01.985506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:08.238084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-03T14:51:14.481701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-03T14:51:42.408739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-03T14:51:42.944867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-03T14:51:43.490411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-03T14:51:43.998543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-03T14:51:44.487635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-03T14:51:21.463700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-03T14:51:22.226131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-03T14:51:22.940798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-03T14:51:23.527067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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7INT-SeriesGaming & AppsMusicT-Series220000000Music of AsiaEntertainment,Music of Asia,Music,Movies1.602680e+09830.0334630.0108791956607444162.095329e+061.522448e+052134569.6251.809830e+062.306178e+061.676330e+062.295416e+064493.984146UCq-Fj5jknLsUf-MWSy4_brA
8USABCkidTV - Nursery RhymesGaming & AppsEducationABCkidTV - Nursery Rhymes138000000MoviesEntertainment,Music,Movies2.209901e+08630.6417160.1160041330253254737.027126e+071.837916e+061837916.0004.891832e+067.052576e+061.265433e+071.572284e+07146.700252UCbCmjCuTUZos6Inko4u57UQ
9INSET IndiaGaming & AppsShowsSET India137000000MoviesEntertainment,TV shows,Music,Movies1.748752e+08790.0012060.0023661217417393171.095729e+05NaN586040.0002.801276e+053.437881e+053.536019e+053.220336e+0576.244316UCpEhnqL0y41EpW2TvWAHD7Q

Last rows

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847CAWatchMojo.comNoneEntertainmentWatchMojo.com24500000EntertainmentEntertainment,TV shows,Movies8.913439e+07790.0071910.004521155670590781.967190e+0526309.37556216.17391366655.63265374661.9791679.539908e+041.060789e+05671.341280UCaWd5_7JhbQBe4dknZhsHJg
848USSuper JoJo - Nursery Rhymes & Kids SongsNaNEducationSuper JoJo - Nursery Rhymes & Kids Songs24400000MoviesMusic,Movies,Entertainment2.809023e+06600.4329240.0267929944180441.095785e+07225979.000175767.000000392456.857143510356.0714296.091040e+056.091040e+0526.613744UCHN9P-CQVBQ1ba8o1NQJVCA
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850USZHCGaming & AppsHowto & StyleZHC24200000TechnologyLifestyle,Technology,Hobby1.496444e+0840.7697140.22037321860155332.691827e+07NaNNaNNaNNaNNaN4.778361e+0649103.751634UClQubH2NeMmGLTLgNdLBwXg
851USDavid GuettaGaming & AppsMusicDavid Guetta24200000Electronic musicMusic,Hip hop music,Pop music,Electronic music2.769015e+07790.1216250.049179158267294672.730757e+06NaNNaNNaN73859.0000001.505350e+051.144851e+061386.875000UC1l7wYrva1qCH-wgqcHaaRg
852NaN1theK (원더케이)MusicMusic1theK (원더케이)24100000Pop musicMusic of Asia,Music,Pop music2.102143e+08740.0147680.004137224316150675.949351e+0469963.00042618.428571150890.000000114119.3400001.361403e+058.631710e+04810.979818UCweOkPb1wVVH0Q0Tlj4a5Pw
853USPost MaloneGaming & AppsMusicPost Malone24000000MusicMusic,Hip hop music,Pop music1.647143e+07571.5294480.069077122524597671.448346e+07NaNNaNNaN106681.0000001.860830e+061.310594e+0611098.637931UCeLHszkByNZtPKcaVXOCOQQ
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21INGoldmines TelefilmsNoneFilm & AnimationGoldmines Telefilms72000000MoviesEntertainment,Movies6.364230e+07690.0224440.006294176520305168.506102e+058.361150e+041.049796e+058.698675e+048.925688e+042.010003e+054.357031e+05495.476870UCyoXW-Dse7fURq30EWl_CUA8